WHERE COGNITIVE SCIENCE MEETS COMPUTER SCIENCE


Keynotes

 

Energy Constraints in Cognitive Processing -
The Role of Constraint Satisfaction in Emergent Awareness

Tuesday, 12 June, 9:10am to 10:10am in East Village 024
Professor Robert Kozma
Biologically-Inspired Neural and Dynamical Systems (BINDS) Lab
Department of CS, UMass Amherst, MA
and
Center for Large-Scale Integration & Optimization (CLION)
Department of Mathematics, U of Memphis, TN
 Recent insight on brain dynamics and cognitive processing provide important clues for the development of artificially intelligent systems with the capability of situation awareness, flexible operation, and rapid response to unpredictable events in dynamically changing and potentially hostile environments. In this talk we analyze the consequences of energy-awareness in developing new AI technologies. Energy constraint is often ignored, or has just a secondary role in typical cutting-edge AI approaches. For example, Deep Learning Neural Networks often require huge amount of data/ time/ parameters/ energy/ computational power, which may not be readily available in various scenarios. Based on neurobiological and cognitive insights, we describe the cinematic model of human cognition, which is analyzed in the context of the energy utilization in our brain. Next we outline mathematical and computational models, which interpret energy constrains as boundary conditions on the system leading to the emergence of oscillatory modes of operation. The resulting spatio-temporal oscillations implement pattern-based computing to realize goal-oriented behavior in an engineering design. Examples from distributed sensing and robot control illustrate the results.
Bio: Dr. Kozma is Professor of Mathematics, University of Memphis, TN, USA; Visiting Professor of CS, University of Massachusetts Amherst. He is Fellow of IEEE and Fellow of the International Neural Network Society (INNS). He is President of INNS, and serves on the Governing Board of IEEE Systems, Man, and Cybernetics Society. He has served on the Ad Com of the IEEE Computational Intelligence Society and on the Board of Governors of INNS. He has been General Chair of IJCNN2009, Atlanta. He is Associate Editor of Neural Networks, Neurocomputing, Cognitive Systems Research, and Cognitive Neurodynamics. Dr. Kozma is the recipient of the "Gabor Award" (2011) of the International Neural Network Society; the "Alumni Association Distinguished Research Achievement Award" (2010); he has been a "National Research Council (NRC) Senior Fellow" at AFRL HAFB (2006-2008).

Neil Armstrong, Jack Welch and Bill Belichick walk into a Bar:
Situational Reasoning for Explanatory Artificial Intelligence

Wednesday, 13 June, 9:00am to 10:00am in East Village 024
Kim A. Mayyasi, CEO
SmartCloud Inc., Boston, MA, USA
 This decade has witnessed extraordinary advances in artificial intelligence (AI) for a broad range of applications - both consumer and industrial. Unfortunately, the promise of AI is reaching a critical juncture whereby so-called "black box" recommender systems are failing to provide useful guidance for operators and managers of complex industrial environments. This growing problem is a consequence of opaque intelligent systems, including machine learning, that do not provide explanations readily understandable to users. Machine learning is powerful; black box machine learning is limited. In fact, the more mission-critical the application, the more "explanatory AI" is required to facilitate human-machine partnering and engender trust. The author advocates a new approach to AI recommender systems using a cognitive model that reflects how experts think about achieving their goals. This is accomplished by extending the Endsley Situational Awareness model through the use of new technologies and techniques. The author believes situational reasoning is the cornerstone for a new wave of "Explanatory AI" systems. After all, experts think in situations, shouldn't our AI?

Mission-Centric Resilient Cyber Defense:
Inspirations from Collective Cognitive Behavior

Thursday, 14 June, 9:00am to 10:00am in East Village 024
Gabriel Jakobson, Chief Scientist
CyberGem Consulting, Inc.
Chestnut Hill, MA, USA
 Everyday practice of cyber defense has revealed that often it is technically inconceivable or financially prohibitive to protect each and every IT component from disruptive events, especially while dealing with large IT infrastructures, or where the IT assets are used in dynamic and unpredictable operational environments. In order to address the above-mentioned issue, research in cyber defense has turned its attention to models of mission-centric cyber defense. The focus of this talk will be on architecture and enabling technologies of mission-centric resilient cyber defense that is based on collective and adaptive behavior of two interacting dynamic processes, cyber situation management in the cyber space, and mission situation management in the physical space. This collective and adaptive behavior assures mission continuation with an acceptable level of trust, even if the IT infrastructure that supports the missions may be compromised, while being under a cyber-attack. In this talk we will discuss how solutions that are based on the principles of mission-centricity in cyber defense and on the models that mimic human cognitive behavior can lead us to cyber attack resilient systems
Bio:Dr Gabriel Jakobson is Chief Scientist at CyberGem Consulting, Inc., a firm specializing in the development of cognitive situation awareness and control solutions for government and business applications. Dr Jakobson has authored over 120 technical publications in expert systems, intelligent databases, real-time event correlations, cyber-attack resilient systems, and cognitive situation control. He received PhD degree in Computer Science from the Institute of Cybernetics, Estonia. He is the chair of the IEEE Man, Systems and Cybernetics Society Committee on Cognitive Situation Management and the Honorary Chair of the IEEE Conferences on Cognitive and Computational Aspects of Situation Management (CogSIMA).

Why Can't We All Just Get Along?

Wednesday, 13 June, at the Conference Banquet
Dr. Henry Lieberman
Massachusetts Institute of Technology
 A vast number of problems with collective decision making in today's society have the same root cause -- misunderstanding the tradeoff between cooperation and competition. We're so busy competing with one another that it's hard to work together to constructively solve the pressing problems of our time. But there's good news. Technological advances like AI and decision support systems will help, but in a surprising way -- by making it possible for people to feel more cooperative with one another. We need to bring our decision-making processes into the 21st century. Bascally, we now use one of two time-worn procedures -- either the democratic "emote and vote", or the authoritarian "plea and decree". Neither forms the best basis for rational problem solving. Inspired by the social processes of the scientific community, and enabled by intelligent decision support tools, we need to better align the values of individuals and organizations. Not only can we "just get along", but we'll be much better able to achieve our shared goals.
Bio: Henry Lieberman is Research Scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His interests are in the intersection of Artificial Intelligence and Human-Computer Interaction, to make computers smarter and more helpful to people. At the Media Lab since 1987, he directed the Media Lab's Software Agents group as Principal Research Scientist.

He served on the board of directors of the Association for the Advancement of Artificial Intelligence (AAAI), the professional organization for AI. He was twice Program Chair of the Intelligent User Interfaces conference. He has edited or co-edited three books, including End-User Development (Springer, 2006), Spinning the Semantic Web (MIT Press, 2004), and Your Wish is My Command: Programming by Example (Morgan Kaufmann, 2001). The fourth, with Christopher Fry, Why Can't We All Just Get Along? (2017) is also the subject of a TEDx talk. He has over 120 academic publications, several Best Paper awards, and 3 US patents. He also consults for industry.

Some of his current projects involve modeling human commonsense reasoning, decision support tools, interactive machine learning, visualization of knowledge and reasoning, and programming environments for education, non-expert users and for development of AI programs. Application areas include social media, multilingual communication, medicine, consumer electronics, management of photo and media libraries, e-commerce, and more.

From 1987-1994 he worked with graphic designer Muriel Cooper on tools for visual thinking, and new graphic metaphors for information visualization and navigation. He holds a strong interest in making programming easier for non-expert users. He is a pioneer of the the technique of Programming by Example, where a user demonstrates examples, which are recorded and generalized using techniques from machine learning. He has also worked on reversible debuggers, visual programming in 2D and 3D, and natural language programming.

From 1972-87, he was a researcher at the MIT Artificial Intelligence Laboratory, now CSAIL. He started with Seymour Papert in the group that originally developed the educational language Logo, and wrote the first bitmap and color graphics systems for Logo, and a seminal graphics algorithm for curve-filling. He also worked with Carl Hewitt on actors, an early object-oriented, parallel language, and invented the notion of prototype object systems and the first real-time garbage collection algorithm, now the basis of most dynamic memory programming languages. He holds a doctoral-equivalent degree (Habilitation) from the University of Paris VI (Sorbonne) and was a Visiting Professor there.

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